How can you develop a data value proposition that supports digital transformation?

1 Identify your data assets

The first step to developing a DVP is to identify what data assets you have, where they come from, and how they are managed. Data assets are any data sources, repositories, or products that you own or have access to, such as customer data, operational data, market data, or analytics outputs. You need to assess the quality, availability, and relevance of your data assets, as well as the risks and costs associated with them. You can use tools such as data catalogs, data dictionaries, or data quality frameworks to document and evaluate your data assets.

2 Define your data value drivers

The next step is to define what value drivers you want to achieve with your data, such as revenue growth, cost reduction, customer satisfaction, innovation, or social impact. Value drivers are the specific outcomes or benefits that you can measure and link to your data assets. You need to prioritize your value drivers based on your business objectives, customer needs, and market opportunities. You can use tools such as value proposition canvas, business model canvas, or SWOT analysis to identify and validate your value drivers.

3 Design your data value proposition

The final step is to design your DVP as a concise and compelling statement that summarizes how you use your data assets to deliver your value drivers. Your DVP should answer three questions: who are your target customers or stakeholders, what are their needs or problems, and how do you solve them with your data assets. Your DVP should also reflect your unique value proposition, differentiators, and competitive advantages. You can use tools such as value proposition statement template, elevator pitch template, or storytelling techniques to craft your DVP.

4 Communicate and align your data value proposition

Once you have developed your DVP, you need to communicate it effectively and align it with your organization's vision, values, and culture. Your DVP should be clear, consistent, and credible, and it should resonate with your internal and external audiences. You need to use various channels and formats to share your DVP, such as presentations, reports, dashboards, or infographics. You also need to involve and engage your stakeholders, such as senior leaders, employees, customers, or partners, to get their feedback and support for your DVP.

5 Implement and monitor your data value proposition

The last step is to implement your DVP and monitor its performance and impact. Your DVP should guide your data strategy, governance, and operations, and it should align with your digital transformation roadmap and initiatives. You need to define and execute your data projects, processes, and policies, and ensure that they deliver the expected value drivers. You also need to measure and report your data value creation, and use metrics, indicators, or scorecards to track and evaluate your DVP.

6 Improve and update your data value proposition

Your DVP is not a static document, but a dynamic and evolving statement that reflects your data maturity and capabilities, as well as the changing needs and expectations of your customers and stakeholders. You need to regularly review and update your DVP, and incorporate new data assets, value drivers, or insights. You also need to identify and address any gaps, challenges, or risks that may affect your DVP, and seek new opportunities or innovations to enhance your DVP.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?